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Indexed by:期刊论文

Date of Publication:2021-03-09

Journal:BUSINESS PROCESS MANAGEMENT JOURNAL

Volume:26

Issue:4,SI

Page Number:925-941

ISSN No.:1463-7154

Key Words:Social network analysis; Influencing factors; Innovation correlation network; Quadratic assignment procedure

Abstract:Purpose The purpose of this paper is to find out the characteristics and evolutionary trends of China's inter-regional innovation correlation network, the status and roles of each province in China's inter-regional innovation correlation network and the influencing factors of China's inter-regional innovation correlation effect. Design/methodology/approach Based on the patent data of 30 provinces (autonomous regions and municipalities) in China from 1991 to 2017, social network analysis was used to find out the characteristics and evolutionary trends of China's inter-regional innovation correlation network and the status and roles of each province in China's inter-regional innovation correlation network. Furthermore, the QAP method was used to find out the influencing factors of China's inter-regional innovation correlation effect. Findings China's inter-regional innovation correlation is becoming increasingly close and inter-regional innovation correlation network is becoming increasingly stable. Jiangsu, Zhejiang, Beijing, Shanghai, Guangdong and other eastern coastal provinces are at the core in the inter-regional innovation correlation network, while the western regions are marginal actors. China's regional innovation development territory can be divided into four blocks, namely, "bidirectional spillover block," "net spillover block," "main beneficial block" and "net beneficial block," and gradient transfer mechanism is obvious between the blocks. The geographical adjacency and similarity in regional industrial structure, urbanization level and government attention degree have significant positive effect on China's inter-regional innovation correlation effects. Research limitations/implications This paper only uses patent application as a measure of regional innovation level to analyze inter-regional innovation correlation effect. Meanwhile, this paper carries out an empirical study only from the provincial level and not from the city level. Practical implications This paper provides the practical basis for further promoting the coordinated development of regional innovation and promoting the construction of regional innovation systems with different characteristics. Originality/value This paper contributes to understand the status and role of each province in inter-regional innovation correlation network. Meanwhile, this paper also helps to understand the influence of the proximity and external environmental factors on inter-regional innovation correlation effect.